update how the energy bias is applied in chi2 and plot-michels

This commit updates how the energy bias is applied when we correct for
the energy bias in correct_energy_bias(). The correct way to apply this
correction is to compute:

    T_corrected = T_reco/(1+bias)

whereas previously we were multiplying by (1-bias).

1 files changed, 3 insertions, 3 deletions

diff --git a/utils/plot-michels b/utils/plot-michels
index bec9e17..b313c3e 100755
--- a/utils/plot-michels
+++ b/utils/plot-michels
@@ -65,16 +65,16 @@ def print_particle_probs(data):
         print("P(%s) = %.2f +/- %.2f" % (particle_id_str,mode[i]*100,std[i]*100))
 
 def make_nll(data, mc, bins, print_nll=False):
+    data_hist = np.histogram(data.ke.values,bins=bins)[0]
+
     def nll(x, grad=None):
         if any(x[i] < 0 for i in (0,2)):
             return np.inf
 
-        data_hist = np.histogram(data.ke.values*(1+x[1]),bins=bins)[0]
-
         # Get the Monte Carlo histograms. We need to do this within the
         # likelihood function since we apply the energy resolution parameters
         # to the Monte Carlo.
-        cdf = norm.cdf(bins[:,np.newaxis],mc.ke.values,mc.ke.values*x[2])
+        cdf = norm.cdf(bins[:,np.newaxis],mc.ke.values*(1+x[1]),mc.ke.values*x[2])
         mc_hist = np.sum(cdf[1:] - cdf[:-1],axis=-1)*x[0]
 
         # Calculate the negative log of the likelihood of observing the data